How Medicare Data influcence our life? When you get hurted (which we don’t wish to happen), how will Medicare system take care of you? To what extent will Medicare System pay you? What is the high cost and low cost contributors to the Medicare system? In our project, we will do researchs on the real world Medicare claims data to get valuable insights on both the data and on parties who are involved in it like healthcare system.

University of Missouri, US – 2 projects:
“RaW”, led by Chao Fang, finding protein substructures to enhance knowledge of protein function using analytics!
“DORK”, led by Xinjian Yao, using analytics to understand Medicare data to further understand what happens in the system.

University Putra Malaysia: Embedded Systems for Public Water Management, led by BALAMI Emmanuel Luke, toword making water supplies more sustainable.

Lumbini Engineering College, Nepal – event held September 28 to inform students about Smarter Planet, Bluemix, Watson, IBM and Students for a Smarter Planet opportunities. Led by Basant Pandey.

We are team “RaW” from University of Missouri – Columbia. We are very excited to have this opportunity to work with IBM company on the smart planet project. Our project introduction is as followed:

Protein is a sequence of amino acids and it always folds into a specific 3D shape. Structures are important to proteins because the functional properties of proteins depend on their 3D structure and structures are more conserved than sequence during the evolution of proteins. Structural Motif is a frequently occurring substructure of proteins. Motifs are thought to be tightly related to protein functions. Identifying motifs from a set of proteins can help us to know their evolutionary history and functions. Our project is to find the frequently occurring substructure using Big Data analytics techniques, in another word, to find one substructure from each protein, that exhibit the highest degree of similarity.

Our project – Real-time Emotion Analysis on Twitter – has completed. We are thinking about the extensibility of our project and we get some ideas.

We use spout and bolt in our project to process data. Spout is in master node and it is like the data file input in Hadoop. Bolt is in worker node and it is like the worker in Hadoop. In our project, the spout is to read Twitter Streaming data and send it to several bolts, which is the process of mapper. And bolt can send data to another bolt. So it is like a chain. Whenever we want to add some new features into our system, you just need to write a new bolt and add it into the processing chain.

In our project, there are two kinds of bolts. The first kind of bolt is to analyze the tweet and extract the useful information and add the emotion value. The the data will be sent to the next bolt, which is responsible to collect and gather the data from several source bolts and publish them into a Redis channel.

So here comes our scalability and extensibility. For the tweet analysis, if we also want to analyze the hashtags as well, all we need to do is to add a kind of bolt in the chain. Then you can either send the result to the reducer or just send the result into another redis channel.

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